In intelligent transportation systems, computer vision and artificial intelligence techniques are applied to traffic data. The traffic data can be acquired by various sensors, see Masaki, “Machine-vision System for Intelligent Transportation: The Autoscope system,” IEEE Transaction Vehicle Technology, Vol. 40, pp. 21-29, 1991. Traffic management and control also rely on sensors for real-time traffic parameter estimation. The dominant technologies for current traffic management systems are loop detectors and pneumatic sensors placed in or on roads to count individual passing vehicles.
Video monitoring systems have more advantages, see Beymer et al., “A Real-time computer vision system for Measuring Traffic Parameters,” CVPR, pp. 495-501, 1997. First, more traffic parameters can be estimated. Second, cameras are less disruptive and less costly to install than loop detectors and pneumatic sensors. For vision-based traffic surveillance system, the cameras are usually mounted on poles or other tall structures looking down at the road. Traffic conditions are captured and digitized into compressed videos, e.g., MPEG. The compressed videos are transmitted to a transportation management center (TMC) for multi-channel statistical analysis and event detection. Beymer uses a grouped sub-feature set to overcome the difficulty of vehicle tracking in congested traffic.
Most computer vision-based systems for monitoring traffic rely on stationary cameras, and inspect traffic by tracking vehicles passing through the field of view of the cameras. In one system, vehicles are located and tracked in 3D as the vehicles move across a ground plane. Trajectories are classified, while taking into account occlusions of vehicles by stationary parts of the scene or occlusions between vehicles, see Sullivan, “Model-based Vision for Traffic Scenes using the Ground-plane Constraint,” In Real-time Computer Vision, D. Terzopoulos and C. Brown (Eds.), Cambridge University Press, 1994.
Another system uses a contour tracker and affine motion model-based Kalman filters to extract vehicle trajectories. A dynamic belief network is used to make inferences about traffic events, see Koller et al., “Towards Robust Automatic Traffic Scene Analysis in Real-time,” ICPR, pp. 126-131, 1994.
Another system detects vehicles in urban traffic scenes by means of rule-based reasoning on visual data, see Cucchiara et al., “Image Analysis and Rule-Based Reasoning for a Traffic Monitoring System,” IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 2, pp. 119-130, 2000. Six types of traffic events are defined and tested in their system.
Kamijo et al., in “Traffic Monitoring and Accident Detection at Intersections,” IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 2, pp. 108-118, 2000, describe an extensible traffic monitoring system for traffic detection at intersection. They use three types of traffic events and a discrete HMM.
Traffic monitoring and vehicle tracking can also be done with a camera mounted on a moving vehicle, see Ferryman et al., “Visual Surveillance for Moving Vehicles,” International Journal of Computer Vision, pp. 187-197, 2000, Dellaert et al., “Model-based car tracking integrated with a road follower,” Proceedings International Conference on Robotics and Automation, pp. 1189-1194, 1998, Dikmanns, “Vehicles Capable of Dynamic Vision,” Proceedings International Joint Conference on Artificial Intelligence, pp. 1577-1592, 1997, and Zhao et al., “Qualitative and Quantitative Car Tracking from a Range Image,” CVPR, pp. 496-501, 1998. Zhao et al. construct three motion models that are incorporated into extended Kalman filters to perform quantitative tracking and motion estimation of both the camera and the vehicles. Dellaert et al. model a vehicle by a box and design a filter to estimate parameters such as vehicle position and dimensions, road curvature and width, vehicle motion, direction and speed, and camera motion. Ferryman et al. estimate the camera motion by matching features on the ground plane from one image to the next. Then, vehicle detection and hypothesis generation is performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image. After vehicle detection and identification, the vehicles are tracked using dynamic filtering.
A rear-end-collision prevention system uses a directional-temporal transform (DTT). That system transforms spatio-temporal image onto a directional-temporal plane, see Jung et al., “Content-Based Event Retrieval Using Semantic Scene Interpretation for Automated Traffic Surveillance,” IEEE Transactions on Intelligent Transportation Systems, Vol. 2, No 3, pp. 151-163, 2001.
A non-parameter regression (NPR) method can be used to forecast traffic events from a signal curve extracted from a moving area, see Shuming et al., “Traffic Incident Detection Algorithm Based on Non-parameter Regression,” IEEE International Conference on Intelligent Transportation Systems (ITS), pp. 714-719, 2002.
Another system uses a multi-level approach, optical flow, Kalman filtering, and blob merging for monitoring traffic, see Maurin et al., “Monitoring Crowded Traffic Scenes,” IEEE International Conference on ITS, pp. 19-24.
Another system extracts traffic information from an MPEG compressed video, and uses a ratio between moving blocks and all blocks to estimate traffic conditions, see Yu et al., “Highway Traffic Information Extraction from Skycam MPEG Video,” IEEE International Conference on ITS, pp. 37-41, 2002.
It is desired to analyze large traffic scenes. Such analysis can yield more information than traditional sensor based systems that detect only single instances of passing vehicles.